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tm-chatbot
Wiki Rasa
Commits
6f4a5bd9
Commit
6f4a5bd9
authored
6 years ago
by
Lucas Schons
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Fix spelling errors
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!71
Documentation: Final Report
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@@ -23,36 +23,36 @@
% To be edited - my (Lukas) suggestion so far
\section
{
Project Description
}
\subsection
{
Convers
t
aional AI and Training
}
\subsection
{
Conversa
t
ional AI and Training
}
Conversational AI describes computer systems that users can interact with by having a
conversation. One important goal is to make the conversation seem as natural as possible.
Ideally, an interacting user should assume to be interacting with another human
beeing
. This
can make communication with a computer become very pleasant and easy for human
ing beeing
s as
they are simply using the
language the always us
e. Besides there is no need for menu
interaction with the system and thus no learning curve
required
.
Ideally, an interacting user should assume to be interacting with another human. This
can make communication with a computer become very pleasant and easy for humans as
they are simply using the
ir natural languag
e. Besides there is no need for menu
interaction with the system and thus no learning curve.
% TODO add example use case (website information)
% TODO add more benefits (24/7 availability)
\\
Conversational AI can be used in Voice Assistants that communicate through spoken words or
through chatbots that imitate a human b
eeing one is chatting with by
text messages.
through chatbots that imitate a human b
y sending
text messages.
\subsection
{
Rasa Framwork
}
Rasa is a collection of
framework
s for conversational AI software. The Rasa Stack contains two
\subsection
{
Rasa Fram
e
work
}
Rasa is a collection of
tool
s for conversational AI software. The Rasa Stack contains two
open source libraries called Rasa NLU and Rasa Core that can be used to create contextual
chatbots. Rasa NLU is a library for natural language understanding with intent classification
and entity extraction Rasa Core is a
C
hatbot framework with machine learning based dialogue
and entity extraction Rasa Core is a
c
hatbot framework with machine learning based dialogue
management. Both can be uses independently but rasa recommends using both.
% TODO add description of how a rasa bot must be trained to achieve results
\subsection
{
Research Question
}
The objective of this project is to find out, wether chatbots can be trained with natural
language texts
\textit
{
automatically
}
. There are two inital research questions: Given that
chatbots need to be trained with knowledge, called facts
.
language texts
\textit
{
automatically
}
. There are two init
i
al research questions: Given that
chatbots need to be trained with knowledge, called facts
:
\begin{itemize}
\item
C
an these facts be extracted from natural language text?
\item
C
an this be done automa
i
tcally?
\item
c
an these facts be extracted from natural language text?
\item
c
an this be done automat
i
cally?
\end{itemize}
\section
{
Solution
Approach
}
\section
{
Approach
}
\subsection
{
Project Goals
}
\subsection
{
Rasa Setup and Intents
}
\subsection
{
Scrapping of Source Texts
}
...
...
@@ -61,11 +61,11 @@
\section
{
Software Architecture
}
\subsection
{
Rasa Chatbot
}
The
Rasa C
hatbot built for this project uses both Rasa Stack components -
\textit
{
Rasa Core
}
and
\textit
{
Rasa NLU
}
. Configuration has been organi
s
ed in reference to examples from the Rasa
github repository.
\\
Rasa NLU has been trained with example questions in
M
arkdown format that
contain highlighted enities. This ensures that the bot to understand intents and
extract the
entities inside the sentences. One example can be seen in listing
\ref
{
nlu
_
example
}
.
\\
The
c
hatbot built for this project uses both Rasa Stack components -
\textit
{
Rasa Core
}
and
\textit
{
Rasa NLU
}
. Configuration has been organi
z
ed in reference to examples from the Rasa
github repository.
\\
Rasa NLU has been trained with example questions in
m
arkdown format that
contain highlighted en
t
ities. This ensures that the bot
is able
to understand intents and
extract the
entities inside the sentences. One example can be seen in listing
\ref
{
nlu
_
example
}
.
\\
\lstinputlisting
[label={nlu_example}, caption={NLU example}]
{
nlu
_
example.md
}
...
...
@@ -74,7 +74,7 @@
contains all actions, entities, slots, intents, and templates the bot deals with.
\textit
{
Templates
}
means template strings for bot utterances.
\textit
{
Slots
}
are variables that can
hold different values. The bot proposed in this project uses a slot to store the name of a
recognized physicist entity
for instance
. According to the Rasa website
recognized physicist entity. According to the Rasa website
\footnote
{
\url
{
https://rasa.com/docs/get
_
started
_
step2/
}}
, the domain is
\textit
{
the universe the bot is living in
}
.
\\
...
...
@@ -90,22 +90,25 @@
conversation ability available.
\begin{center}
\begin{tabular}
{
| c | l | l |
}
\hline
No
&
Intent
&
Example
\\
\hline
1
&
birthdate
&
When was Albert Einstein born
\\
\hline
2
&
nationality
&
Where was Albert Einstein born
\\
\hline
3
&
day of death
&
When did Albert Einstein die
\\
\hline
4
&
place of death
&
Where did Albert Einstein die
\\
\hline
5
&
is alive
&
Is Albert Einstein still alive
\\
\hline
6
&
spouse
&
Who was Albert Einstein married to
\\
\hline
7
&
primary education
&
Where did Albert Einstein go to school
\\
\hline
8
&
university
&
Which university did Albert Einstein attend
\\
\hline
9
&
area of research
&
What was Albert Einstein area of research
\\
\hline
10
&
workplace
&
Where did Albert Einstein work
\\
\hline
11
&
awards
&
What awards did Albert Einstein win
\\
\hline
\end{tabular}
\label
{
table:intent
_
table
}
\begin{table}
\begin{tabular}
{
| c | l | l |
}
\hline
No
&
Intent
&
Example
\\
\hline
1
&
birthdate
&
When was Albert Einstein born
\\
\hline
2
&
nationality
&
Where was Albert Einstein born
\\
\hline
3
&
day of death
&
When did Albert Einstein die
\\
\hline
4
&
place of death
&
Where did Albert Einstein die
\\
\hline
5
&
is alive
&
Is Albert Einstein still alive
\\
\hline
6
&
spouse
&
Who was Albert Einstein married to
\\
\hline
7
&
primary education
&
Where did Albert Einstein go to school
\\
\hline
8
&
university
&
Which university did Albert Einstein attend
\\
\hline
9
&
area of research
&
What was Albert Einstein area of research
\\
\hline
10
&
workplace
&
Where did Albert Einstein work
\\
\hline
11
&
awards
&
What awards did Albert Einstein win
\\
\hline
\end{tabular}
\caption
{
Intents that are recognized by the bot
}
\label
{
table:intent
_
table
}
\end{table}
\end{center}
\subsection
{
R Package 'wikiproc'
}
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